AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

Abstract

Background: Protein aggregation correlates with the development of several debilitating human disorders of growing incidence, such as Alzheimer's and Parkinson's diseases. On the biotechnological side, protein production is often hampered by the accumulation of recombinant proteins into aggregates. Thus, the development of methods to anticipate the aggregation properties of polypeptides is receiving increasing attention. AGGRESCAN is a web-based software for the prediction of aggregation-prone segments in protein sequences, the analysis of the effect of mutations on protein aggregation propensities and the comparison of the aggregation properties of different proteins or protein sets.

Rational design of potent domain antibody inhibitors of amyloid fibril assembly

“…Therefore, we evaluated the impact of the Aβ12-21 and Aβ33-42 gammabodies on the relative solvent accessibility of N-terminal (Aβ residues 3-10), middle (Aβ residues [18][19][20][21][22], and C-terminal (Aβ residues 30-36) Aβ peptide segments during fibrillization using a proteolytic assay that we have reported previously (10). We find that the solvent accessibility of the hydrophilic N terminus of Aβ is unchanged during Aβ fibrillization (days 0-6), and that the Aβ12-21 and Aβ33-42 gammabodies do not alter its solvent accessibility (Fig.…”

“…Therefore, we selected the peptide hormone IAPP that forms amyloidogenic aggregates associated with type 2 diabetes (18), and the protein α-Synuclein that forms aggregates linked to Parkinson's disease (19). We identified 10-residue amyloidogenic peptide segments in IAPP (residues 22-NFGAILSSTN-31) and α-Synuclein (residues 69-AVVTGVTAVA-78) that are predicted to mediate amyloid formation of each polypeptide by multiple algorithms (20)(21)(22)(23)(24). Grafting these peptide segments into CDR3 (along with negatively charged residues at each edge of CDR3; SI Methods) yielded single-domain (V H ) gammabodies that are well-expressed (>20 mg/L) and fail to aggregate when heated.…”

Classification of mismatch repair gene missense variants with PON-MMR

“…All the variants were entered to protein aggregation predictors Aggrescan [Conchillo-Sole et al, 2007] and Waltz [Oliveberg, 2010]. The interatomic contacts of variants in MSH2 and MSH6 protein structure were checked with CMA (Contact Map Analysis) [Sobolev et al, 2005], CSU (Contacts of Structural Units) [Sobolev et al, 1999], and RankViaContact [Shen and Vihinen, 2003].…”

Boosting antibody developability through rational sequence optimization

“…12,22 A number of different software packages have been developed to predict cross-beta-sheet aggregation-propensities based on the sequence. [23][24][25][26][27][28] There is no single computational method that is able to estimate antibody developability and shelf-life, but a number of established methods, developed for different purposes, can be utilized to assemble important aspects of rate-limiting steps of antibody degradation pathways and provide valuable estimations for antibody developability. Computational methods pinpoint potential liabilities to distinct sequence patterns, paving the way for rational engineering toward improved developability.…”